class_docstr: Abstract Layer for tensorflow_text tokenizers.
  
  Input shape:
    N-D (2D as default) tensor with shape: `(batch_size, input_length)`.
  
  Output shape:
    (N+1)-D (3D as default) tensor with shape:
      `(batch_size, input_length, token_dim)`.
code: def __init__(
      self, tokenizer_instance, pad_value, squeeze_token_dim, **kwargs
  ):
      if (
          kwargs.get("dtype") is not None
          and kwargs.get("dtype") != "string"
      ):
          raise ValueError(
              "The only valid dtype for %s is string, but got: %s"
              % (self.__class__.__name__, kwargs.get("dtype"))
          )
      kwargs["dtype"] = "string"
      super(TokenizerBase, self).__init__(**kwargs)
  
      self._tokenizer = tokenizer_instance
      self._pad_value = pad_value
      self._squeeze_token_dim = squeeze_token_dim
  
imports: import os
  import tensorflow
  from tensorflow.python.ops import lookup_ops
  from tensorflow.python.ops.ragged import ragged_conversion_ops
  from tensorflow_text.python.ops import unicode_script_tokenizer
  from tensorflow_text.python.ops import whitespace_tokenizer
  from tensorflow_text.python.ops import wordpiece_tokenizer
label_desc: Use image node_id 1 to create a new TokenizerBase object from inherited base classes: tf.keras.layers with example: obj = TokenizerBase(tokenizer_instance, pad_value, squeeze_token_dim)